Research Projects A-Z

11/5/18
Extending the work that was completed for year one funding related to “Developing Highway Safety Performance Metrics in an Advanced Connected Vehicle Environment Utilizing Near-Crash Events from the SHRP 2 Naturalistic Driving Study.”
11/5/18
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
6/14/19
Developing models that will predict the delay a passenger car or a truck is likely to encounter by the time the vehicle arrives at the border.
1/12/18
Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.
4/15/19
This project develops novel data mining methodologies that integrate heterogeneous urban data for the estimation of city-wide transportation information.
11/15/19
Pooling P3 project data from various sources to build a database that can be analyzed and used to inform future decision making.
11/15/19
Developing a smartphone-based travel behavior data collection platform that recruits participants by rewarding users with real-time parking information.
11/2/17
Analyzing traffic violations and traffic crash records to develop a probabilistic model that will help detect high risk drivers with the main goal of preventing future crashes.
11/2/17
Conducting a detailed, multivariate statistical assessment of pavement treatments by public-private partnerships, and studying their performance in terms of extending pavement lives.
4/30/19
Developing a novel green navigation system, called Green Nav, that gives a driver the most fuel efficient route for his vehicle as opposed to the shortest or fastest route.
11/10/17
Examining in-vehicle and infrastructure-based technologies to assess how they might impact emergency responders, particularly EMS.
11/5/18
Developing a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests.
11/15/19
Exploring historical incident and traffic data to revolutionize response strategies.
9/23/19

The customized Lincoln MKZ will help boost the university’s research enterprise in connected and autonomous vehicles.

3/12/19
Creating a mobile computer application for documenting and sharing data regarding vehicular accidents.
11/15/19
The recent network disruptions in the Washington Metro system showed the new reality associated with aging transit infrastructure and highlighted the potential severity of such disruptions. However, relevant studies in the literature are limited and agencies need more empirical evidence to help them better planning and implementing maintenance work.
3/12/19
Developing the tools needed to process immense amounts of data, develop new performance metrics based on the data collected, and propose methods to enhance performance.
4/15/19
Exploring the potential for using a number of machine learning and data mining methods to analyze accident data.
3/12/19
The project investigates how real-time conditions interact to affect driver safety performance changes. From that understanding, practitioners and drivers can make more informed decisions to reduce the likelihood of a crash.
3/12/19
Incorporating data analytics into paratransit planning and operations is a promising approach for increasing their cost-effectiveness.
12/1/17
Creating a quality-aware crowdsourced road sensing system that integrates sensory data from multiple vehicles while placing more weight on the vehicles that provide high quality data to significantly improve integration accuracy.
3/12/19
The project proposes a deep learning model to predict the best recharging recommendation including best recharging time and location for eTaxi drivers.
11/15/19
Recently, there has been an unprecedented interest in Connected and Automated Vehicles (CAVs) or self-driving vehicles. CAVs have the potential to revolutionize transportation, resulting in a major paradigm shift in the way we move and move our goods. The current project is conducted in synergy with another project at UB, funded by New York State Energy and Research Development Authority (NYSERDA) and New York State Department of Transportation (NYSDOT). That project is evaluating the technical feasibility, safety and reliability of using CAV technology, and in particular the self-driving shuttle, Olli, manufactured by Local Motors. 
11/15/19
Mining social media data to deduce useful information about present or future travelers’ behavior, with a special emphasis under events, including both planned and unplanned.
11/15/19
Conventional travel demand and other planning data sources provided very limited coverage on non-motorized modes such as biking and pedestrian. Crowd-sourcing approach has the potential to collect more up-to-date data for these modes with minimal costs and at a continuous basis. However, such data is mostly self-reported and lacks a unified format and standard, which compromises the data quality. More advanced data processing, cleansing, and integration methods are needed to make such data sources useful and valuable. This study investigated a set of biking incidents data collected in the Washington D.C. metropolitan area to explore such potentials.
3/12/19
The project suggests a bottom-up travel behavior driven approach which obtains trends in individual travel behavior first and use such information to enhance longitudinal origin-destination demand monitoring.
11/5/18
Integrating machine learning, big data, sensor networks, and agent-based transportation modeling to prototype an algorithm that combines the power of a model-driven approach with the power of big data.